Semi-Automatic Ontology Generation for Infectious Disease Domain from Text Data

The accurate representation and organization of knowledge in the domain of infectious diseases are crucial for effective disease management, research, and public health interventions. However, the vast amount of textual data, including scientific literature, clinical reports, and online resources, poses challenges in extracting and structuring relevant information. This paper presents an approach for ontology generation of infectious diseases from text data i.e. atlas of human infectious disease, aiming to capture and formalize the key concepts, relationships, and properties in a structured knowledge representation. The proposed methodology facilitates the automated extraction and classification of relevant information from textual sources. 0The resulting ontology provides a structured framework for organizing infectious disease knowledge, enabling efficient data integration, interoperability, and advanced reasoning capabilities. Furthermore, the AHIDO ontology could be integrated with other related ontology i.e. PROSPECT-IDR to support risk calculation of disease. The application of ontology generation in the infectious disease domain has the potential to enhance disease surveillance, inform clinical decision-making, and support research efforts for improved understanding and control of infectious diseases.